| ---
|
| tags:
|
| - sentence-transformers
|
| - sentence-similarity
|
| - feature-extraction
|
| - generated_from_trainer
|
| - dataset_size:350
|
| - loss:MultipleNegativesRankingLoss
|
| base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
|
| widget:
|
| - source_sentence: Data pengeluaran bulanan rumah tangga pedesaan untuk konsumsi makanan
|
| dan non-makanan per provinsi, tahun berapa saja tersedia?
|
| sentences:
|
| - Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 (84 x 84)
|
| - Persentase RataRata Pengeluaran per Kapita Sebulan Untuk Makanan dan Bukan Makanan
|
| di Daerah Perdesaan Menurut Provinsi, 2007-2024
|
| - Nilai Impor Jawa Madura Menurut Pelabuhan Impor di Pulau Jawa Madura Tahun 2009
|
| - 2013 (Juta US $) 1)
|
| - source_sentence: Asal impor gula Indonesia periode 2017 hingga 2023
|
| sentences:
|
| - Banyaknya Anggota Kadinda Menurut Kabupaten/Kota di Provinsi Jawa Tengah, 2019
|
| - Impor Gula menurut Negara Asal Utama, 2017-2023
|
| - Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Kelompok Umur,
|
| 2023
|
| - source_sentence: Laju kehilangan hutan Indonesia dalam dan luar kawasan hutan 2013-2022.
|
| sentences:
|
| - Institusi Pemerintah Neraca Institusi Terintegrasi (Triliun Rupiah), 2016 2023
|
| - Angka Deforestasi (Netto) Indonesia di Dalam dan di Luar Kawasan Hutan Tahun 2013-2022
|
| (Ha/Th)
|
| - Produksi Perkebunan Menurut Kabupaten/Kota dan Jenis Tanaman di Provinsi Jawa
|
| Tengah (ton), 2021 dan 2022
|
| - source_sentence: Kemana saja lada putih Indonesia diekspor pada periode 2012 sampai
|
| 2023?
|
| sentences:
|
| - Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Kelompok Umur,
|
| 2022-2023
|
| - Ekspor Lada Putih menurut Negara Tujuan Utama, 2012-2023
|
| - Angka Kelahiran Kasar (Crude Birth Rate) Hasil Long Form SP2020 Menurut Provinsi/Kabupaten/Kota,
|
| 2020
|
| - source_sentence: data gaji bersih pegawai per bulan tahun 2023 berdasarkan pendidikan
|
| dan jenis pekerjaan utama
|
| sentences:
|
| - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi
|
| yang Ditamatkan dan Jenis Pekerjaan Utama, 2023
|
| - Banyaknya Kunjungan Kapal Melalui Pelabuhan Jepara Menurut Jenis Pelayaran Tahun
|
| 2009 - 2013
|
| - Ekspor Sarang Burung menurut Negara Tujuan Utama, 2012-2023
|
| pipeline_tag: sentence-similarity
|
| library_name: sentence-transformers
|
| metrics:
|
| - cosine_accuracy@1
|
| - cosine_accuracy@3
|
| - cosine_accuracy@5
|
| - cosine_accuracy@10
|
| - cosine_precision@1
|
| - cosine_precision@3
|
| - cosine_precision@5
|
| - cosine_precision@10
|
| - cosine_recall@1
|
| - cosine_recall@3
|
| - cosine_recall@5
|
| - cosine_recall@10
|
| - cosine_ndcg@10
|
| - cosine_mrr@10
|
| - cosine_map@100
|
| model-index:
|
| - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
|
| results:
|
| - task:
|
| type: information-retrieval
|
| name: Information Retrieval
|
| dataset:
|
| name: bps val mfd all
|
| type: bps-val-mfd-all
|
| metrics:
|
| - type: cosine_accuracy@1
|
| value: 0.9861111111111112
|
| name: Cosine Accuracy@1
|
| - type: cosine_accuracy@3
|
| value: 0.9861111111111112
|
| name: Cosine Accuracy@3
|
| - type: cosine_accuracy@5
|
| value: 0.9861111111111112
|
| name: Cosine Accuracy@5
|
| - type: cosine_accuracy@10
|
| value: 0.9861111111111112
|
| name: Cosine Accuracy@10
|
| - type: cosine_precision@1
|
| value: 0.9861111111111112
|
| name: Cosine Precision@1
|
| - type: cosine_precision@3
|
| value: 0.9351851851851851
|
| name: Cosine Precision@3
|
| - type: cosine_precision@5
|
| value: 0.9055555555555554
|
| name: Cosine Precision@5
|
| - type: cosine_precision@10
|
| value: 0.8333333333333334
|
| name: Cosine Precision@10
|
| - type: cosine_recall@1
|
| value: 0.016151592322246593
|
| name: Cosine Recall@1
|
| - type: cosine_recall@3
|
| value: 0.0425075387306992
|
| name: Cosine Recall@3
|
| - type: cosine_recall@5
|
| value: 0.06836160354671791
|
| name: Cosine Recall@5
|
| - type: cosine_recall@10
|
| value: 0.11202747994449548
|
| name: Cosine Recall@10
|
| - type: cosine_ndcg@10
|
| value: 0.8706665539282586
|
| name: Cosine Ndcg@10
|
| - type: cosine_mrr@10
|
| value: 0.9861111111111112
|
| name: Cosine Mrr@10
|
| - type: cosine_map@100
|
| value: 0.44673547368787836
|
| name: Cosine Map@100
|
| ---
|
|
|
| # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
|
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the csv dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
|
|
|
| ## Model Details
|
|
|
| ### Model Description
|
| - **Model Type:** Sentence Transformer
|
| - **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d -->
|
| - **Maximum Sequence Length:** 128 tokens
|
| - **Output Dimensionality:** 384 dimensions
|
| - **Similarity Function:** Cosine Similarity
|
| - **Training Dataset:**
|
| - csv
|
| <!-- - **Language:** Unknown -->
|
| <!-- - **License:** Unknown -->
|
|
|
| ### Model Sources
|
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
|
|
| ### Full Model Architecture
|
|
|
| ```
|
| SentenceTransformer(
|
| (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
|
| (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| )
|
| ```
|
|
|
| ## Usage
|
|
|
| ### Direct Usage (Sentence Transformers)
|
|
|
| First install the Sentence Transformers library:
|
|
|
| ```bash
|
| pip install -U sentence-transformers
|
| ```
|
|
|
| Then you can load this model and run inference.
|
| ```python
|
| from sentence_transformers import SentenceTransformer
|
|
|
| # Download from the 🤗 Hub
|
| model = SentenceTransformer("sentence_transformers_model_id")
|
| # Run inference
|
| sentences = [
|
| 'data gaji bersih pegawai per bulan tahun 2023 berdasarkan pendidikan dan jenis pekerjaan utama',
|
| 'Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi yang Ditamatkan dan Jenis Pekerjaan Utama, 2023',
|
| 'Banyaknya Kunjungan Kapal Melalui Pelabuhan Jepara Menurut Jenis Pelayaran Tahun 2009 - 2013',
|
| ]
|
| embeddings = model.encode(sentences)
|
| print(embeddings.shape)
|
| # [3, 384]
|
|
|
| # Get the similarity scores for the embeddings
|
| similarities = model.similarity(embeddings, embeddings)
|
| print(similarities.shape)
|
| # [3, 3]
|
| ```
|
|
|
| <!--
|
| ### Direct Usage (Transformers)
|
|
|
| <details><summary>Click to see the direct usage in Transformers</summary>
|
|
|
| </details>
|
| -->
|
|
|
| <!--
|
| ### Downstream Usage (Sentence Transformers)
|
|
|
| You can finetune this model on your own dataset.
|
|
|
| <details><summary>Click to expand</summary>
|
|
|
| </details>
|
| -->
|
|
|
| <!--
|
| ### Out-of-Scope Use
|
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| -->
|
|
|
| ## Evaluation
|
|
|
| ### Metrics
|
|
|
| #### Information Retrieval
|
|
|
| * Dataset: `bps-val-mfd-all`
|
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
|
|
| | Metric | Value |
|
| |:--------------------|:-----------|
|
| | cosine_accuracy@1 | 0.9861 |
|
| | cosine_accuracy@3 | 0.9861 |
|
| | cosine_accuracy@5 | 0.9861 |
|
| | cosine_accuracy@10 | 0.9861 |
|
| | cosine_precision@1 | 0.9861 |
|
| | cosine_precision@3 | 0.9352 |
|
| | cosine_precision@5 | 0.9056 |
|
| | cosine_precision@10 | 0.8333 |
|
| | cosine_recall@1 | 0.0162 |
|
| | cosine_recall@3 | 0.0425 |
|
| | cosine_recall@5 | 0.0684 |
|
| | cosine_recall@10 | 0.112 |
|
| | **cosine_ndcg@10** | **0.8707** |
|
| | cosine_mrr@10 | 0.9861 |
|
| | cosine_map@100 | 0.4467 |
|
|
|
| <!--
|
| ## Bias, Risks and Limitations
|
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| -->
|
|
|
| <!--
|
| ### Recommendations
|
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| -->
|
|
|
| ## Training Details
|
|
|
| ### Training Dataset
|
|
|
| #### csv
|
|
|
| * Dataset: csv
|
| * Size: 350 training samples
|
| * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
|
| * Approximate statistics based on the first 350 samples:
|
| | | query | positive | negative |
|
| |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
|
| | type | string | string | string |
|
| | details | <ul><li>min: 7 tokens</li><li>mean: 16.16 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 23.2 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 27.02 tokens</li><li>max: 59 tokens</li></ul> |
|
| * Samples:
|
| | query | positive | negative |
|
| |:--------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|
|
| | <code>Bagaimana pengeluaran rumah tangga per orang di Indonesia berubah dari 2010 sampai 2024?</code> | <code>Distribusi Pembagian Pengeluaran per Kapita dan Indeks Gini, 2010-2024</code> | <code>Proyeksi Beban Pencemaran Udara Menurut Industri di Jawa Tengah Tahun 2020 (Ton/Tahun)</code> |
|
| | <code>Data kesenjangan pendapatan di Indonesia tahun 2010-2024: indeks Gini dan pengeluaran rata-rata.</code> | <code>Distribusi Pembagian Pengeluaran per Kapita dan Indeks Gini, 2010-2024</code> | <code>Banyaknya Mahasiswa dan Dosen Pada Perguruan Tinggi Agama Islam Swasta di Jawa Tengah, 2018/2019</code> |
|
| | <code>Berapa konsumsi makanan pokok per orang per minggu di Indonesia tahun 2007-2024?</code> | <code>Rata-Rata Konsumsi per Kapita Seminggu Beberapa Macam Bahan Makanan Penting, 2007-2024</code> | <code>Rekapitulasi Industri Non Formal Yang Baru Menurut Kabupaten/kota 2012</code> |
|
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
|
| ```json
|
| {
|
| "scale": 20.0,
|
| "similarity_fct": "cos_sim"
|
| }
|
| ```
|
|
|
| ### Training Hyperparameters
|
| #### Non-Default Hyperparameters
|
|
|
| - `eval_strategy`: steps
|
| - `per_device_train_batch_size`: 32
|
| - `per_device_eval_batch_size`: 32
|
| - `weight_decay`: 0.01
|
| - `warmup_ratio`: 0.1
|
| - `fp16`: True
|
| - `load_best_model_at_end`: True
|
|
|
| #### All Hyperparameters
|
| <details><summary>Click to expand</summary>
|
|
|
| - `overwrite_output_dir`: False
|
| - `do_predict`: False
|
| - `eval_strategy`: steps
|
| - `prediction_loss_only`: True
|
| - `per_device_train_batch_size`: 32
|
| - `per_device_eval_batch_size`: 32
|
| - `per_gpu_train_batch_size`: None
|
| - `per_gpu_eval_batch_size`: None
|
| - `gradient_accumulation_steps`: 1
|
| - `eval_accumulation_steps`: None
|
| - `torch_empty_cache_steps`: None
|
| - `learning_rate`: 5e-05
|
| - `weight_decay`: 0.01
|
| - `adam_beta1`: 0.9
|
| - `adam_beta2`: 0.999
|
| - `adam_epsilon`: 1e-08
|
| - `max_grad_norm`: 1.0
|
| - `num_train_epochs`: 3
|
| - `max_steps`: -1
|
| - `lr_scheduler_type`: linear
|
| - `lr_scheduler_kwargs`: {}
|
| - `warmup_ratio`: 0.1
|
| - `warmup_steps`: 0
|
| - `log_level`: passive
|
| - `log_level_replica`: warning
|
| - `log_on_each_node`: True
|
| - `logging_nan_inf_filter`: True
|
| - `save_safetensors`: True
|
| - `save_on_each_node`: False
|
| - `save_only_model`: False
|
| - `restore_callback_states_from_checkpoint`: False
|
| - `no_cuda`: False
|
| - `use_cpu`: False
|
| - `use_mps_device`: False
|
| - `seed`: 42
|
| - `data_seed`: None
|
| - `jit_mode_eval`: False
|
| - `use_ipex`: False
|
| - `bf16`: False
|
| - `fp16`: True
|
| - `fp16_opt_level`: O1
|
| - `half_precision_backend`: auto
|
| - `bf16_full_eval`: False
|
| - `fp16_full_eval`: False
|
| - `tf32`: None
|
| - `local_rank`: 0
|
| - `ddp_backend`: None
|
| - `tpu_num_cores`: None
|
| - `tpu_metrics_debug`: False
|
| - `debug`: []
|
| - `dataloader_drop_last`: False
|
| - `dataloader_num_workers`: 0
|
| - `dataloader_prefetch_factor`: None
|
| - `past_index`: -1
|
| - `disable_tqdm`: False
|
| - `remove_unused_columns`: True
|
| - `label_names`: None
|
| - `load_best_model_at_end`: True
|
| - `ignore_data_skip`: False
|
| - `fsdp`: []
|
| - `fsdp_min_num_params`: 0
|
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
| - `fsdp_transformer_layer_cls_to_wrap`: None
|
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
| - `deepspeed`: None
|
| - `label_smoothing_factor`: 0.0
|
| - `optim`: adamw_torch
|
| - `optim_args`: None
|
| - `adafactor`: False
|
| - `group_by_length`: False
|
| - `length_column_name`: length
|
| - `ddp_find_unused_parameters`: None
|
| - `ddp_bucket_cap_mb`: None
|
| - `ddp_broadcast_buffers`: False
|
| - `dataloader_pin_memory`: True
|
| - `dataloader_persistent_workers`: False
|
| - `skip_memory_metrics`: True
|
| - `use_legacy_prediction_loop`: False
|
| - `push_to_hub`: False
|
| - `resume_from_checkpoint`: None
|
| - `hub_model_id`: None
|
| - `hub_strategy`: every_save
|
| - `hub_private_repo`: None
|
| - `hub_always_push`: False
|
| - `hub_revision`: None
|
| - `gradient_checkpointing`: False
|
| - `gradient_checkpointing_kwargs`: None
|
| - `include_inputs_for_metrics`: False
|
| - `include_for_metrics`: []
|
| - `eval_do_concat_batches`: True
|
| - `fp16_backend`: auto
|
| - `push_to_hub_model_id`: None
|
| - `push_to_hub_organization`: None
|
| - `mp_parameters`:
|
| - `auto_find_batch_size`: False
|
| - `full_determinism`: False
|
| - `torchdynamo`: None
|
| - `ray_scope`: last
|
| - `ddp_timeout`: 1800
|
| - `torch_compile`: False
|
| - `torch_compile_backend`: None
|
| - `torch_compile_mode`: None
|
| - `include_tokens_per_second`: False
|
| - `include_num_input_tokens_seen`: False
|
| - `neftune_noise_alpha`: None
|
| - `optim_target_modules`: None
|
| - `batch_eval_metrics`: False
|
| - `eval_on_start`: False
|
| - `use_liger_kernel`: False
|
| - `liger_kernel_config`: None
|
| - `eval_use_gather_object`: False
|
| - `average_tokens_across_devices`: False
|
| - `prompts`: None
|
| - `batch_sampler`: batch_sampler
|
| - `multi_dataset_batch_sampler`: proportional
|
|
|
| </details>
|
|
|
| ### Training Logs
|
| | Epoch | Step | bps-val-mfd-all_cosine_ndcg@10 |
|
| |:----------:|:------:|:------------------------------:|
|
| | 0.9091 | 10 | 0.8300 |
|
| | **1.8182** | **20** | **0.8736** |
|
| | 2.7273 | 30 | 0.8707 |
|
|
|
| * The bold row denotes the saved checkpoint.
|
|
|
| ### Framework Versions
|
| - Python: 3.10.11
|
| - Sentence Transformers: 3.4.0
|
| - Transformers: 4.53.1
|
| - PyTorch: 2.7.1+cpu
|
| - Accelerate: 1.8.1
|
| - Datasets: 3.6.0
|
| - Tokenizers: 0.21.2
|
|
|
| ## Citation
|
|
|
| ### BibTeX
|
|
|
| #### Sentence Transformers
|
| ```bibtex
|
| @inproceedings{reimers-2019-sentence-bert,
|
| title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
| author = "Reimers, Nils and Gurevych, Iryna",
|
| booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
| month = "11",
|
| year = "2019",
|
| publisher = "Association for Computational Linguistics",
|
| url = "https://arxiv.org/abs/1908.10084",
|
| }
|
| ```
|
|
|
| #### MultipleNegativesRankingLoss
|
| ```bibtex
|
| @misc{henderson2017efficient,
|
| title={Efficient Natural Language Response Suggestion for Smart Reply},
|
| author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
|
| year={2017},
|
| eprint={1705.00652},
|
| archivePrefix={arXiv},
|
| primaryClass={cs.CL}
|
| }
|
| ```
|
|
|
| <!--
|
| ## Glossary
|
|
|
| *Clearly define terms in order to be accessible across audiences.*
|
| -->
|
|
|
| <!--
|
| ## Model Card Authors
|
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| -->
|
|
|
| <!--
|
| ## Model Card Contact
|
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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| --> |
